Image-based time series forecasting: A deep convolutional neural network approach.

Inspired by the successful use of deep learning in computer vision, in this paper we introduce ForCNN, a novel deep learning method for univariate time series forecasting that mixes convolutional and dense layers in a single neural network. Instead of using conventional, numeric representations of time series data as input to the network, the proposed method considers visual representations of it in the form of images to directly produce point forecasts. Three variants of deep convolutional neural networks are examined to process the images, the first based on VGG-19, the second on ResNet-50, while the third on a self-designed architecture.

The effect of challenge-based gamification on learning: An experiment in the context of statistics education.

Gamification is increasingly employed in learning environments as a way to increase student motivation and consequent learning outcomes. However, while the research on the effectiveness of gamification in the context of education has been growing, there are blind spots regarding which types of gamification may be suitable for different educational contexts. This study investigates the effects of the challenge-based gamification on learning in the area of statistics education.

Impact of a green roof system on indoor fungal aerosol in a primary school in Greece.

A primary school was investigated for airborne fungi by a culture-based method, in classrooms underneath a green roof in comparison to conventional concrete roofs. A portable Burkard sampler was used for the collection of air samples onto petri dishes with 2% Malt Extract Agar. The fungal aerosol mean concentration was 71 CFU m (range 17-176 CFU m, median 51) in the classroom directly under the green roof, significantly lower than 192-228 CFU m (range 0-1090 CFU m, median 69) under the concrete roofs and 188-412 CFU m (range 0-2183 CFU m, median 771) in ground floor classrooms.

Chemical characterization, sources and potential health risk of PM and PM pollution across the Greater Athens Area.

With the principal aim to assess the typical Mediterranean profile of the PM and PM pollution, three intensive monitoring campaigns took place simultaneously within different types of environment across an urban location of the basin. Focusing on the PM components with numerous anthropogenic sources and increased potential health risk, the samples were chemically analyzed for 20 p.m.

Improving the forecasting performance of temporal hierarchies.

Temporal hierarchies have been widely used during the past few years as they are capable to provide more accurate coherent forecasts at different planning horizons. However, they still display some limitations, being mainly subject to the forecasting methods used for generating the base forecasts and the particularities of the examined series. This paper deals with such limitations by considering three different strategies: (i) combining forecasts of multiple methods, (ii) applying bias adjustments and (iii) selectively implementing temporal hierarchies to avoid seasonal shrinkage.